Learning a Perceptual Quality Metric for Correlation in Scatterplots

Wöhler, Leslie; Zou, Yuxin; Mühlhausen, Moritz; Albuquerque, Georgia; Magnor, Marcus

Learning a Perceptual Quality Metric for Correlation in Scatterplots

Files

055-062.pdf (656.12 KB)

Date

2019

Authors

Wöhler, Leslie
Zou, Yuxin
Mühlhausen, Moritz
Albuquerque, Georgia
Magnor, Marcus

Publisher

The Eurographics Association

Abstract

Visual quality metrics describe the quality and efficiency of multidimensional data visualizations in order to guide data analysts during exploration tasks. Current metrics are usually based on empirical algorithms which do not accurately represent human perception and therefore often differ from the analysts' expectations. We propose a new perception-based quality metric using deep learning that rates the correlation of data dimensions visualized by scatterplots. First, we created a data set containing over 15,000 pairs of scatterplots with human annotations on the perceived correlation between the data dimensions. Afterwards, we trained two different Convolutional Neural Networks (CNN), one extracts features from scatterplot images and the other directly from data vectors. We evaluated both CNNs on our test set and compared them to previous visual quality metrics. The experiments show that our new metric is able to represent human perception more accurately than previous methods.

        @inproceedings{10.2312:vmv.20191318
,
booktitle = {Vision, Modeling and Visualization
},
editor = {Schulz, Hans-Jörg and Teschner, Matthias and Wimmer, Michael
},
title = {{Learning a Perceptual Quality Metric for Correlation in Scatterplots
}},
author = {Wöhler, Leslie and 
Zou, Yuxin and 
Mühlhausen, Moritz and 
Albuquerque, Georgia and 
Magnor, Marcus
},
year = {2019
},
publisher = {The Eurographics Association
},
ISBN = {978-3-03868-098-7
},
DOI = {10.2312/vmv.20191318
}
}